Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Minimal shape and intensity cost path segmentation.

Dieter Seghers1, Dirk Loeckx, Frederik Maes

  • 1Group of Medical Image Computing (Radiology-ESAT/PSI), Faculties of Engineering, University Hospital Gasthuisberg, B-3000 Leuven, Belgium. dieter.seghers@uz.kuleuven.ac.be

IEEE Transactions on Medical Imaging
|August 19, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Real-World Evaluation of AI-Assisted Readout of Skin Prick Automated Test Results.

Allergy·2026
Same author

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.

Scientific reports·2025
Same author

Automated Classification of Neuromuscular Diseases Using Thigh Muscle MRI With Model Interpretations.

Journal of cachexia, sarcopenia and muscle·2025
Same author

Artificial Intelligence (AI)-assisted readout method for the evaluation of skin prick automated test results.

Nature communications·2025
Same author

Automated quantification of lung pathology on micro-CT in diverse disease models using deep learning.

EBioMedicine·2025
Same author

Local response function estimation in spherical deconvolution for comprehensive tissue representation using diffusion MRI.

Imaging neuroscience (Cambridge, Mass.)·2025
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

A novel model-based segmentation algorithm simultaneously optimizes shape and intensity, outperforming active shape models (ASM). This method enhances medical image segmentation accuracy, particularly for pathological cases.

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Computational Anatomy

Background:

  • Accurate segmentation of anatomical structures is crucial for medical diagnosis and treatment planning.
  • Existing methods like Active Shape Models (ASM) iteratively process shape and intensity information.
  • A need exists for more efficient and robust segmentation algorithms, especially for complex or pathological cases.

Purpose of the Study:

  • To introduce a new generic model-based segmentation algorithm.
  • To develop a method that optimizes shape and intensity characteristics simultaneously.
  • To improve upon the performance of Active Shape Models (ASM) in medical image segmentation.

Main Methods:

  • A novel generic model-based segmentation algorithm trained from examples.

Related Experiment Videos

  • Simultaneous optimization of shape and gray-level appearance using a single cost function.
  • Non-iterative optimization via dynamic programming, eliminating the need for initialization.
  • Integration of local gray-level appearance at landmark points and landmark-specific statistical shape models.

    • Main Results:

    • The proposed algorithm demonstrated significantly higher performance compared to ASM schemes in segmenting anatomical structures.
    • Validation was performed on chest and hand radiographs, showing robust results.
    • The method effectively utilizes both shape and intensity information for improved segmentation accuracy.

    Conclusions:

    • The new algorithm offers a highly effective and robust approach to medical image segmentation.
    • Its ability to handle pathological cases and ease of implementation make it valuable for clinical applications.
    • Simultaneous optimization of shape and intensity provides superior performance over iterative ASM methods.